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Tier-1 brand, mid-level ML role, metro location, and broad skillset create high applicant competition.
Highly specialized ML research in ad fraud reduces cross-industry transferability.
Explicit years, internet-scale ML, research and big-data requirements make shortlisting highly strict.
Develop and implement advanced machine learning algorithms to detect sophisticated invalid traffic (IVT) in advertising at petabyte scale.
Analyze large, unstructured datasets to generate insights and improve fraud detection methodologies under strict latency constraints.
Mentor junior scientists and produce research reports adhering to top-tier external publication standards.
At least 2 years of experience as a data/research scientist, statistician, or quantitative analyst in an internet-based company handling complex and big data sources.
At least 1 year experience with data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical/mathematical software (e.g., R, SAS, Matlab).
Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or equivalent STEM work experience.
At least 1 year experience creating or contributing to mathematical textbooks, research papers, or educational content.
Experienced in deploying machine learning solutions and statistical techniques for fraud detection in large-scale internet advertising environments.
Comfortable working with cloud-based big data technologies like EC2, S3, EMR, Sagemaker, RedShift and handling petabyte-scale datasets.
Research-oriented with the ability to innovate detection methodologies and publish work in top-tier venues.